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README.md

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+---
+license: mit
+language:
+- en
+---
+
+# TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data
+
+Paper: https://arxiv.org/abs/2401.13223
+
+Code: https://github.com/fengbinzhu/TAT-LLM
+
+
+## Introduction
+
+We present TAT-LLM, a specialized language model crafted through the innovative Step-wise Pipeline approach, focusing on the nuanced realm of tabular and textual question answering (QA). This model is the fruit of rigorously fine-tuning the LLaMA 2 architecture with a novel dataset, autonomously generated from expertly annotated resources. TAT-LLM stands at the intersection of tabular comprehension and textual analysis, engineered to excel by embodying three fundamental phases: Extraction, Reasoning, and Execution. Our empirical findings illuminate TAT-LLM's remarkable capability to eclipse traditional benchmarks, surmounting even the most advanced models and colossal language models such as GPT-4 across a suite of demanding QA tasks like FinQA, TAT-QA, and TAT-DQA. This endeavor not only sets a new standard for task-specific language models but also paves the way for future explorations in optimizing smaller models for highly specialized functions.
+
+| Model | Size | FINQA | TATQA | TATDQA |
+| ---   | ---  | ---   | ---   | ---    |
+| GPT-3.5-Turbo | - | 58.00 | 59.47 | 52.74 |
+| GPT-4 | - | 63.91 | 71.92 | 64.46 |
+| TAT-LLM-7B | 7B | 65.13 | 76.49 | 71.38 |
+| TAT-LLM-13B | 13B | 71.93 | 77.51 | 72.22 |
+| TAT-LLM-70B | 70B | **76.81** | **81.42** | **76.55** |
+
+
+## Training
+
+We train our TAT-LLM model in various sizes, including 7B, 13B, and 70B, by fine-tuning LLaMA 2 using Low-Rank Adaptation (LoRa) on a combination of the train sets from FinQA, TAT-QA and TAT-DQA datasets. To refine accuracy, we introduce an External Executor, enhancing the model by processing intermediate outputs to derive conclusive answers. Please refer to the [paper](https://arxiv.org/abs/2401.13223) for more details.
+
+## Inference & Evaluation
+
+Please refer to code [here](https://github.com/fengbinzhu/TAT-LLM)
+
+## Citation
+
+If you find this repository helpful, please consider citing our paper:
+
+```
+@misc{zhu2024tatllm,
+      title={TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data},
+      author={Fengbin Zhu and Ziyang Liu and Fuli Feng and Chao Wang and Moxin Li and Tat-Seng Chua},
+      year={2024},
+      eprint={2401.13223},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/Llama-2-13b-hf",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "lora_alpha": 16,
+  "lora_dropout": 0.05,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "k_proj",
+    "v_proj",
+    "o_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.bin

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